Redefining Development through Logistics performance and ESG metrics
Description
This dataset was created to empirically test the hypothesis that logistics performance, environmental sustainability, governance quality, and sustainable development progress are interdependent drivers of economic output, specifically GDP per capita. The data is used in the paper “Redefining Development through Logistics Performance and ESG Metrics.” Research Hypothesis: ESG-related metrics—specifically the Environmental Performance Index (EPI), Sustainable Development Goals Index (SDG), and Worldwide Governance Indicators (WGI)—have a significant and positive influence on national logistics performance (LPI). Logistics performance, when combined with ESG indicators, explains a significant portion of cross-country variation in GDP per capita. Data Contents: The dataset includes normalized, cross-sectional data from 123 countries, covering the following variables: Logistics Performance Index (LPI) and its six components: customs (EFF), infrastructure (QUAL), international shipments (EASE), logistics quality (COM), tracking (TR), and timeliness (FREQ). Environmental Performance Index (EPI) – 2024 version from Yale University Sustainable Development Goals Index (SDG) – 2024 global dataset Worldwide Governance Indicators (WGI) – mean and six sub-indicators from the World Bank (2023) GDP per capita (in current USD) and its natural log transformation (lnGDP) All variables were normalized to a common scale (0–100) for comparability. WGI indicators were rescaled from their original –2.5 to +2.5 range. Notable Findings: All ESG indicators were significantly and positively associated with logistics performance (LPI), with WGI exerting the strongest influence. The combination of LPI, EPI, SDG, and WGI explained 81.7% of the variance in GDP per capita across countries. Governance quality (WGI) was the strongest predictor of both LPI and GDP. Fuzzy Cognitive Mapping (FCM) simulations showed that improving governance and environmental metrics creates positive feedback loops in logistics and economic outcomes. Data Collection Sources: World Bank (2023): LPI and WGI data Yale & Columbia University (2024): Environmental Performance Index (EPI) Dublin University (Sachs, Lafortune, Fuller) (2024): SDG Index World Bank Open Data: GDP per capita (2023) Use and Interpretation: Variables are standardized (0–100); higher values always indicate better performance. The data can be used for replication, policy simulation, comparative country analysis, and machine learning on development outcomes. Analysts can perform linear regression, correlation, or fuzzy systems modeling using this dataset. Recommended tools: SPSS, R, Python (Pandas), or Mental Modeler (for FCM simulation
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Objective and Structure The study aimed to quantify the extent to which ESG indicators—namely the Environmental Performance Index (EPI), the Sustainable Development Goals Index (SDG), and the Worldwide Governance Indicators (WGI)—influence logistics performance (LPI), and to assess how these variables collectively shape GDP per capita. To achieve this, high-quality cross-sectional data were compiled for 123 countries, normalized to allow comparability, and structured to support advanced modeling. The dataset is fully transparent, reproducible, and suitable for reuse in policy evaluation, academic research, or comparative development studies. 2. Sources and Variable Selection All variables were obtained from internationally recognized, open-access datasets: LPI (Logistics Performance Index) – World Bank (2023) WGI (Worldwide Governance Indicators) – World Bank (2023) GDP per capita (current US$) – World Bank (2023) EPI (Environmental Performance Index) – Yale & Columbia University (2024) SDG Index – Sustainable Development Solutions Network (2024) The selected indicators are widely used in development economics and policy planning, and each possesses well-documented methodology, global coverage, and regular updates. 3. Inclusion Criteria and Country Sample The dataset includes only countries for which full data were available across all five core indicators. This resulted in a final sample of 123 countries, ensuring statistical validity and preventing imputation or estimation bias. Territories, microstates, and conflict zones with missing or incomplete data were excluded. Each row in the dataset corresponds to one country, and each column represents an original or derived variable.4. Normalization and Data Processing To harmonize the scale of all variables, normalization procedures were applied using Microsoft Excel 365.5. Software and Tools The following software tools were used: Microsoft Excel 365 – for data integration, cleaning, and normalization. IBM SPSS v23 – for descriptive statistics, regression analysis, correlation testing, and diagnostic validation Mental Modeler – for the construction and simulation of Fuzzy Cognitive Maps included R² and Adjusted R², Durbin-Watson tests for autocorrelation, and VIF for multicollinearity. All models were statistically significant at p < 0.001. The strongest predictor of GDP was WGI, followed by LPI, SDG, and EPI. These findings support the integrated hypothesis of co-dependence. Correlation analysis used Kendall’s tau-b, due to non-normality in several variables as identified by the Shapiro–Wilk and Kolmogorov–Smirnov tests. Strong positive correlations were observed between governance and logistics subcomponents. 7. Fuzzy Cognitive Mapping (FCM) To capture feedback loops and simulate development scenarios, a Fuzzy Cognitive Map was developed.
Institutions
- Geoponoko Panepistemio Athenon Schole Epharmosmenon Oikonomikon kai Koinonikon Epistemon